FORECASTING WITH MIXED FREQUENCY DATA:MIDAS VERSUS STATE SPACE DYNAMIC FACTOR MODEL: AN APPLICATION TO FORECASTING SWEDISH GDP GROWTH
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Most macroeconomic activity series such as Swedish GDP growth are collected quarterly while an important proportion of time series are recorded at a higher frequency. Thus, policy and business decision makers are often confront with the problems of forecasting and assessing current business and economy state via incomplete statistical data due to publication lags. In this paper, we survey a few general methods and examine different models for mixed frequency issues. We mainly compare mixed data sampling regression (MIDAS) and state space dynamic factor model (SS-DFM) by the comparison experiments forecasting Swedish GDP growth with various economic indicators. We find that single-indicator MIDAS is a wise choice when the explanatory variable is coincident with the target series; that an AR term enables MIDAS more promising since it considers autoregressive behaviour of the target series and makes the dynamic construction more flexible; that SS-DFM and M-MIDAS are the most outstanding models and M-MIDAS dominates undoubtedly at short horizons up to 6 months, whereas SS-DFM is more reliable at long predictive horizons. And finally we conclude that there is no perfect winner because each model can dominate in a special situation.
Place, publisher, year, edition, pages
2013. , 30 p.
mixed frequency data, MIDAS regression, state space model, dynamic factor model, Swedish GDP growth
Probability Theory and Statistics
IdentifiersURN: urn:nbn:se:oru:diva-29475ISRN: ORU-HHS/STA-AS-2013/0010--SEOAI: oai:DiVA.org:oru-29475DiVA: diva2:627732
Subject / course
UppsokSocial and Behavioural Science, Law
Karlsson, Sune, Professor
Mantalos, Panagiotis, Docent